

import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			XLMTokenizer
						_A    :				List[Any]       					=			False





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									a_						:						Optional[Any] 							= [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									a_						:						Dict 							= dict(zip(_lowercase  ,      range(len(_lowercase    )    )    )    )
									a_						:						int 							= ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

									a_						:						List[Any] 							= os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES["""vocab_file"""]    )
									a_						:						Tuple 							= os.path.join(self.tmpdirname  ,      VOCAB_FILES_NAMES["""merges_file"""]    )
									with open(self.vocab_file  ,      """w"""    ) as fp:
												fp.write(json.dumps(_lowercase    )    )
									with open(self.merges_file  ,      """w"""    ) as fp:
												fp.write("""\n""".join(_lowercase    )    )





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Optional[Any]:
									a_						:						int 							= """lower newer"""
									a_						:						str 							= """lower newer"""
									return input_text, output_text





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Optional[int] 							= XLMTokenizer(self.vocab_file  ,      self.merges_file    )

									a_						:						Dict 							= """lower"""
									a_						:						Dict 							= ["""low""", """er</w>"""]
									a_						:						Optional[Any] 							= tokenizer.tokenize(_lowercase    )
									self.assertListEqual(_lowercase  ,      _lowercase    )

									a_						:						List[Any] 							= tokens + ["""<unk>"""]
									a_						:						Tuple 							= [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase    )  ,      _lowercase    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Optional[int] 							= XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""    )

									a_						:						int 							= tokenizer.encode("""sequence builders"""  ,      add_special_tokens=_lowercase    )
									a_						:						Optional[Any] 							= tokenizer.encode("""multi-sequence build"""  ,      add_special_tokens=_lowercase    )

									a_						:						Dict 							= tokenizer.build_inputs_with_special_tokens(_lowercase    )
									a_						:						Any 							= tokenizer.build_inputs_with_special_tokens(_lowercase  ,      _lowercase    )

									assert encoded_sentence == [0] + text + [1]
									assert encoded_pair == [0] + text + [1] + text_a + [1]





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case				:      Optional[int]   		=			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMForMultipleChoice""",
	    """XLMForQuestionAnswering""",
	    """XLMForQuestionAnsweringSimple""",
	    """XLMForSequenceClassification""",
	    """XLMForTokenClassification""",
	    """XLMModel""",
	    """XLMPreTrainedModel""",
	    """XLMWithLMHeadModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMForMultipleChoice""",
	    """TFXLMForQuestionAnsweringSimple""",
	    """TFXLMForSequenceClassification""",
	    """TFXLMForTokenClassification""",
	    """TFXLMMainLayer""",
	    """TFXLMModel""",
	    """TFXLMPreTrainedModel""",
	    """TFXLMWithLMHeadModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	__snake_case				:      Union[str, Any]   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


